Building a Digital Respiratory Disease Framework for COPD management in Central Appalachia

项目总结/文摘

PROJECT SUMMARY/ABSTRACT Compared to urban residents, rural communities have higher rates of chronic obstructive pulmonary disease (COPD) with worse disease control, resulting in higher rates of morbidity and mortality. In general, rural residents suffer from higher poverty and uninsured rates, further compounded by geographic isolation and limited access to quality healthcare. This and other social factors have resulted in higher incidence and worse outcomes of nearly every chronic disease. Telehealth holds promise to address these challenges and expand access to specialist care in these communities, but qualitative telehealth visits often lack necessary patient data for physicians to make informed management decisions. Telemedicine is also often hindered by limited broadband access in rural areas. Wearable technologies for remote monitoring suffer similar limitations, and, due to cost and burdensome daily engagement requirements, lead to poor adherence rates in an older, sicker population. To address these challenges, Medentum is developing an affordable and accessible, multi-functional, home-use device featuring a camera, low-cost sensors (temperature, pulse oximeter) and a digital stethoscope. A companion phone application allows the recording of demographics, sociocultural data, medical history, and symptoms, and guides the patient to collect their biometric readings with the handheld device. Without the necessity of broadband, the patient can transmit their information securely to a remote physician/chronic disease specialist who can make a diagnosis and treatment plan. For this SBIR, Medentum will adapt its device and software platform for respiratory disease, layering it with artificial intelligence (AI) algorithms, to create a digital respiratory disease framework (DRDF) that empowers self-management of COPD. The aims of this SBIR are to 1) study the usability and feasibility of this COPD respiratory framework in a rural Central Appalachian population of 75 patients in Southwest Virginia and 2) build preliminary AI algorithms that autonomously predict COPD exacerbation risk by analyzing low burden variables including risk factors (social, behavioral, environmental), symptoms, COPD Assessment tests, and device biometrics (temperature, pulse, oxygen saturation, respiration rate and breath sounds). In Phase II, the platform will incorporate smart triggers that will alert patients if certain environmental risk factors are met, prompting them to engage with the platform. The smart algorithms will then automatically predict their COPD exacerbation risk, ultimately triaging them to appropriate treatment. Predicting and pre-empting disease exacerbation by facilitating early connections to medical providers for prompt and effective treatment will have an enormous impact on health outcomes and treatment costs for rural COPD patients. Ultimately, this platform will augment COPD self-management, promote preventive respiratory care, and disseminate evidence-based COPD treatments, all designed to reduce the significant health disparities in a remote, underserved Central Appalachian population with worse respiratory outcomes.